Most of the plots are interactive, you can click or zoom to get more details ! Also don’t hesitate to click on plots, they will zoom automatically !
# Loading Packages
library(data.table)
library(lubridate)
library(tidyverse)
library(esquisse)
library(plyr)
library(ggplot2)
library(cowplot)
library(naniar) #for NA exploration
library(sp) #spatial data
library(reshape2)
library(plotly)
library(gissr)
library(leaflet)
library(leaflet.providers)
library(geosphere)
library(DT)Those are required packages
Loading the dataset called “LaptopSales_red.csv” given for the Homework
FALSE Classes 'data.table' and 'data.frame': 148786 obs. of 17 variables:
FALSE $ V1 : int 171289 38634 260048 166045 243280 118859 249957 198058 198850 267007 ...
FALSE $ Date : chr "9/20/2008 2:49" "5/30/2008 9:52" "12/10/2008 9:26" "9/15/2008 9:41" ...
FALSE $ Configuration : int 528 307 235 168 517 738 301 301 479 472 ...
FALSE $ Customer.Postcode : chr "NW5 1SP" "N6 6BU" "CR0 2BW" "WC2H 9PS" ...
FALSE $ Store.Postcode : chr "N3 1DH" "N3 1DH" "CR7 8LE" "SW1P 3AU" ...
FALSE $ Retail.Price : int 413 515 315 NA 580 535 455 465 600 392 ...
FALSE $ Screen.Size..Inches. : int 17 15 15 15 17 17 15 15 17 17 ...
FALSE $ Battery.Life..Hours. : int 4 6 5 5 4 6 6 6 4 4 ...
FALSE $ RAM..GB. : int 2 1 2 1 2 1 1 1 1 1 ...
FALSE $ Processor.Speeds..GHz.: num 2.4 2 2.4 2 2.4 2 1.5 1.5 2.4 2.4 ...
FALSE $ Integrated.Wireless. : chr "No" "Yes" "No" "Yes" ...
FALSE $ HD.Size..GB. : int 300 80 80 300 120 40 120 120 300 300 ...
FALSE $ Bundled.Applications. : chr "No" "Yes" "Yes" "No" ...
FALSE $ customer.X : int 528771 528281 532781 530190 537350 532498 533130 529390 533998 532498 ...
FALSE $ customer.Y : int 186041 187336 166444 181139 169306 168334 182489 181270 168421 168334 ...
FALSE $ store.X : int 525109 525109 532714 529902 528739 528739 534057 528924 528739 532714 ...
FALSE $ store.Y : int 190628 190628 168302 179641 173080 173080 179682 178440 173080 168302 ...
FALSE - attr(*, ".internal.selfref")=<externalptr>
Retail Price is the only variable missing at rate of approximately 4%
This barplot shows the most frequent retail prices for all stores in 2018
We can interpret this boxplot as the mean or median retail price of the 2018 Computer Dataset, click on the white sphere to get the mean !
## [1] "Last Recorded Prices are 406 USD and 530 USD on the same Day with a mean of 468 USD"
Here is given the last recorded prices for 2018
Those Plot shows different aggregations levels, can be used depending on the analysis we want, thus the granularity need.
Each box plots belongs to a specific stores, we can see a common trend across all stores in 2018
Looking at times series, we can see that not all stores have the same time trend, but most of them do.
FALSE `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Using an smooth approximator, we can see two differents trends, first a rapid increase in price while being at low configurations, and then the slope tend to stay constant and low, ending with a increase with highest configurations.
Enjoy looking at each stores and customers in London UK ! You can find there exact location by clicking on them !
The following barplots show two ways of analyzing the stores sales results: by the number of transactions or the sales revenues they each generated during 2018.
Each Unique Customer can be found here, scroll down and see the distance they need to travel to get to their store.
You can see the proportional revenues participation of each stores in 2018.
With this multiple facets barplots, you can spot which configuration is less or not sold depending on the store.